mlsanityData to 14 Oct 2026

A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data

arXiv 2010.12493 ↗First posted 23 Oct 2020cs.LG stat.MLcited by 31

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Abstract (arXiv)

Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences. ISTS commonly appears in healthcare, economics, and geoscience. Especially in the medical environment, the widely used Electronic Health Records (EHRs) have abundant typical irregularly sampled medical time series (ISMTS) data. Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management. However, it is challenging to directly use deep learning models for ISMTS data. On the one hand, ISMTS data has the intra-series and inter-series relations. Both the local and global structures should be considered. On the other hand, methods should consider the trade-off between task accuracy and model complexity and remain generality and interpretability. So far, many existing works have tried to solve the above problems and have achieved good results. In this paper, we review these deep learning methods from the perspectives of technology and task. Under the technology-driven perspective, we summarize them into two categories - missing data-based methods and raw data-based methods. Under the task-driven perspective, we also summarize them into two categories - data imputation-oriented and downstream task-oriented. For each of them, we point out their advantages and disadvantages. Moreover, we implement some representative methods and compare them on four medical datasets with two tasks. Finally, we discuss the challenges and opportunities in this area.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Oct 2020.

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Citing papers per year: 2021: 2; 2022: 4; 2023: 6; 2024: 8; 2025: 5; 2026: 6.

31 papers.

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  1. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 169
  2. 2022TACTiS: Transformer-Attentional Copulas for Time Seriescited by 38
  3. 2024Universal Time-Series Representation Learning: A Surveycited by 30
  4. 2025MIRA: Medical Time Series Foundation Model for Real-World Health Datacited by 22
  5. 2025Comprehensive Review of Neural Differential Equations for Time Series Analysiscited by 20
  6. 2022A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sourcescited by 13
  7. 2021TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Datacited by 10
  8. 2023IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solverscited by 5
  9. 2022Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networkscited by 4
  10. 2024MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Recordscited by 4
  11. 2024Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamicscited by 4
  12. 2022COPER: Continuous Patient State Perceivercited by 2
19 more
  1. 2024Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement using Electronic Health Recordscited by 2
  2. 2024Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosiscited by 2
  3. 2021Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Networkcited by 1
  4. 2023Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Seriescited by 1
  5. 2023Individualized Dynamic Latent Factor Model for Multi-resolutional Data with Application to Mobile Healthcited by 1
  6. 2023Curricular and Cyclical Loss for Time Series Learning Strategycited by 1
  7. 2023Curriculum Design Helps Spiking Neural Networks to Classify Time Seriescited by 1
  8. 2024Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samplescited by 1
  9. 2024Review of Data-centric Time Series Analysis from Sample, Feature, and Periodcited by 1
  10. 2024Incorporating Taylor Series and Recursive Structure in Neural Networks for Time Series Predictioncited by 0
  11. 2025Evaluating Time Series Models with Knowledge Discoverycited by 0
  12. 2025Closing Gaps: An Imputation Analysis of ICU Vital Signscited by 0
  13. 2025VitalBench: A Rigorous Multi-Center Benchmark for Long-Term Vital Sign Prediction in Intraoperative Carecited by 0
  14. 2026Aortic Valve Disease Screening from PPG via Physiology-Guided Self-Supervised Learningcited by 0
  15. 2026DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classificationcited by 0
  16. 2026Interpretable Machine Learning for Antepartum Prediction of Pregnancy-Associated Thrombotic Microangiopathy Using Routine Longitudinal Laboratory Datacited by 0
  17. 2026MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classificationcited by 0
  18. 2026TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMscited by 0
  19. 2026CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecastingcited by 0